Consistency of quantitative electroencephalography features in a large clinical data set.

Objective: Despite their increasing use and public health importance, little is known about the consistency and variability of the quantitative features of baseline electroencephalography (EEG) measurements in healthy individuals and populations. This study aims to investigate population consistency of EEG features.

Approach: We propose a non-parametric method of evaluating consistency of commonly used EEG features based on counts of non-significant statistical tests using a large data set. We first replicate stationarity results of absolute band powers using coefficients of variation. We then determine feature stationarity, intra-subject consistency, inter-subject consistency, and intra- versus inter-subject consistency across different epoch lengths for 30 features.

Main Results: We find in general that features with normalizing constants are more stationary. We also find entropy, median, skew, and kurtosis of EEG to behave as baseline EEG metrics. However, other spectral and signal shape features have stronger intra-subject consistency and thus are better for distinguishing individuals.

Significance: These results provide data-driven non-parametric methods of identifying EEG features and their spatial characteristics ideal for various EEG applications, and determining future EEG feature consistencies using an existing EEG data set.